NodeJS vs. JAVA: Which Backend Language Wins The Battle?

In the history of computing, one revolutionary year was 1995, when JAVA made it to the headlines. Closely following its heels, JavaScript appeared in the same year. Both backend technologies sound more like twins name-wise, however, nothing is as distinct as JAVA and JavaScript. Despite their technical differences, they have somehow managed to collide as tech stacks, all thanks to NodeJS. 

Today, enterprises of all scales and sizes prefer to hire NodeJS developers for their projects. You don’t believe me? Hear this out. Netflix recently migrated from JAVA to NodeJS. 

But Why? 

Because NodeJS is not only faster but has a flexible development process along with scalable benefits. But that does not make JAVA any less of a master. There are tons of areas where JAVA still rules as a backend. 

All we need to do is, look at their individual benefits and then decide which backend technology can serve better for different projects?

But before we make a head-to-head comparison of NodeJS and JAVA, let’s take an overview of both the technologies to clear our basics. 

JAVA: An Overview

JAVA is a traditional programming language that works on object-oriented concepts, after C++. It follows the principle of “write once and run anywhere,” which states that the code once written can be used anywhere without the need for recompilation. 

The language is highly secure and stable, and it is perfect for eCommerce, transportation, FinTech, and banking services. There are four significant platforms of JAVA:

1. JAVA FX
2. JAVA, Standard edition (JAVA SE)
3. JAVA, Micro edition (JAVA ME)
4. JAVA, Enterprise edition (JAVA EE)

Currently, JAVA is empowering the web with almost 3.4% of the websites. It is also ranked amongst the top 5 master programming languages. 

NodeJS: An Overview

NodeJS is a JavaScript-based runtime environment working as a server-side open-source tool. NodeJS runs on the single-threaded technique that delivers scalable and high-performing results. You can easily extend the capability of your project by using NodeJS based frameworks like Socket.io, Meteor.js, and Express. 

NodeJS is developed in push-based architecture. Hence, the framework is expert in making SPAs (Single Page Applications), API services, and complex websites. 

Currently, more than 43% of enterprises hire NodeJS developers to develop business-grade applications.  

NodeJS is also rated as one of the most loved tools in the Developer survey report of 2020. 

Now that we are familiar with both the backend technologies let’s quickly get into the comparison mode. For a balanced comparison, we will use some significant metrics such as: 

1. Performance
2. Architectural structure
3. Testing

Performance comparison of Java and NodeJS:

Performance is an unavoidable factor in any website or application development. 

Java- 

• Programs written in Java are in the form of Byte codes which state that the language performs much better than any other traditional technology. 
• These byte code instructions are quickly taken by the virtual machines, hence, resulting in smooth performance. 
• To deliver high-performing applications, Java has also implemented components like a “just-in-time compiler.” 

NodeJS- 

• NodeJS is massively popular for its non-blocking and asynchronous structure that helps in creating a perfect environment to run micro-operations. 
• These backend operations process in multithreading; hence, they do not impact the main application thread. 
• NodeJS utilizes a V8 JavaScript engine, which delivers fast results in the least time possible. 

 Architectural Structure:

When it comes to choosing a framework, every product owner tries to avoid strict guidelines or architectural protocols. Let’s see which one is more flexible in terms of architecture. 

Java-

• With Java, developers tend to follow the MVC (Model View Controller) architectural pattern.
• This facilitates hassle-free testing and easy maintenance of code. 
• Since developers work individually on the design, one change in a module doesn’t impact the entire application. 
• Hence, you save yourself from a lot of rework and documentation processes with Java. 

NodeJS- 

• NodeJS is modern and advanced and leverages a single-threaded event loop architectural system.
• This system manages multiple concurrent requests altogether without interfering with the performance. 
• NodeJS also allows you to follow the MVC architecture pattern to ease out onboarding issues in the code. 
• The best part is that the technology encourages asynchronous communication between the components. 

Testing:

To be able to work without any defects or glitches is a blessing to development projects. Customers today expect continuous and fast-loading websites and applications. Let’s see which one passes the testing phase efficiently. 

Java- 

• Developers can easily create test cases with Java. 
• These cases include grouping, sequencing, data-driven features. In addition to this, one can also create parallel tests. 
• The language is compatible with multiple testing tools and frameworks like- FitNess, Apache JMeter, Selenium, JUnit, etc. 

NodeJS-

• NodeJS has rich debugging capabilities for competency testing. 
• It creates a sound ecosystem for applications with tools like Mocha, Jasmine, AVA, Lab, etc. 
• The critical feature is compliance with testing libraries like Chai and Mocha to provide a seamless user experience. 

Several other factors like scalability, database support, and microservice architecture can help weigh the benefits of both the backend technologies.  

But the primary dilemma is when and where to use Java and NodeJS in your projects. 

You can consider NodeJS under these conditions:

1. Your application supports web streaming content. 
2. You require a performant single-page application.
3. Your app should contain efficient data processing capabilities. 
4. You expect a real-time multi-user web app. 
5. Your project is a browser-based gaming app. 

You can consider Java under these conditions:

1. You want to develop an enterprise-grade application. 
2. You are looking for rich community support for accessible services. 
3. Your business is Big Data or eCommerce. 
4. You need mature technology with security features. 
5. You wish to develop a cryptocurrency application with security functions. 

Conclusion

Since every technology serves individual features and benefits, there is no master to rule all. Thus, everything relies on your project requirements. We hope the above factors helped you overcome your backend tech dilemma. If you wish to explore more technical insights, you can hire full-stack developers that are expert in NodeJS and Java. They might make your development process even smoother. 

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Face Detection Explained: State-of-the-Art Methods and Best Tools

So many of us have used different Facebook applications to see us aging, turned into rock stars, or applied festive make-up. Such waves of facial transformations are usually accompanied by warnings not to share images of your faces – otherwise, they will be processed and misused.

But how does AI use faces in reality? Let’s discuss state-of-the-art applications for face detection and recognition.

First, detection and recognition are different tasks. Face detection is the crucial part of face recognition determining the number of faces on the picture or video without remembering or storing details. It may define some demographic data like age or gender, but it cannot recognize individuals.

Face recognition identifies a face in a photo or a video image against a pre-existing database of faces. Faces indeed need to be enrolled into the system to create the database of unique facial features. Afterward, the system breaks down a new image intro key features and compares them against the information stored in the database.

First, the computer examines either a photo or a video image and tries to distinguish faces from any other objects in the background. There are methods that a computer can use to achieve this, compensating for illumination, orientation, or camera distance. Yang, Kriegman, and Ahuja presented a classification for face detection methods. These methods are divided into four categories, and the face detection algorithms could belong to two or more groups.

Knowledge-based face detection

This method relies on the set of rules developed by humans according to our knowledge. We know that a face must have a nose, eyes, and mouth within certain distances and positions with each other. The problem with this method is to build an appropriate set of rules. If the rules are too general or too detailed, the system ends up with many false positives. However, it does not work for all skin colors and depends on lighting conditions that can change the exact hue of a person’s skin in the picture.

Template matching

The template matching method uses predefined or parameterized face templates to locate or detect the faces by the correlation between the predefined or deformable templates and input images. The face model can be constructed by edges using the edge detection method. 

A variation of this approach is the controlled background technique. If you are lucky to have a frontal face image and a plain background, you can remove the background, leaving face boundaries. 

For this approach, the software has several classifiers for detecting various types of front-on faces and some for profile faces, such as detectors of eyes, a nose, a mouth, and in some cases, even a whole body. While the approach is easy to implement, it is usually inadequate for face detection.

Feature-based face detection

The feature-based method extracts structural features of the face. It is trained as a classifier and then used to differentiate facial and non-facial regions. One example of this method is color-based face detection that scans colored images or videos for areas with typical skin color and then looks for face segments.

Haar Feature Selection relies on similar properties of human faces to form matches from facial features: location and size of the eye, mouth, bridge of the nose, and the oriented gradients of pixel intensities. There are 38 layers of cascaded classifiers to obtain the total number of 6061 features from each frontal face. You can find some pre-trained classifiers here.

Source

Histogram of Oriented Gradients (HOG) is a feature extractor for object detection. The features extracted are the distribution (histograms) of directions of gradients (oriented gradients) of the image. 

Gradients are typically large round edges and corners and allow us to detect those regions. Instead of considering the pixel intensities, they count the occurrences of gradient vectors to represent the light direction to localize image segments. The method uses overlapping local contrast normalization to improve accuracy.

Appearance-based face detection

The more advanced appearance-based method depends on a set of delegate training face images to find out face models. It relies on machine learning and statistical analysis to find the relevant characteristics of face images and extract features from them. This method unites several algorithms:

Eigenface-based algorithm efficiently represents faces using Principal Component Analysis (PCA). PCA is applied to a set of images to lower the dimension of the dataset, best describing the variance of data. In this method, a face can be modeled as a linear combination of eigenfaces (set of eigenvectors). Face recognition, in this case, is based on the comparing of coefficients of linear representation. 

Distribution-based algorithms like PCA and Fisher’s Discriminant define the subspace representing facial patterns. They usually have a trained classifier that identifies instances of the target pattern class from the background image patterns.

Hidden Markov Model is a standard method for detection tasks. Its states would be the facial features, usually described as strips of pixels. 

Sparse Network of Winnows defines two linear units or target nodes: one for face patterns and the other for non-face patterns.

Naive Bayes Classifiers compute the probability of a face to appear in the picture based on the frequency of occurrence of a series of the pattern over the training images.

Inductive learning uses such  algorithms as  Quinlan’s  C4.5 or Mitchell’s FIND-S to detect faces starting with the most specific hypothesis and generalizing. 

Neural networks, such as GANs, are among the most recent and most powerful methods for detection problems, including face detection, emotion detection, and face recognition.

Video Processing: Motion-based face detection

In video images, you can use movement as a guide. One specific face movement is blinking, so if the software can determine a regular blinking pattern, it determines the face. 

Various other motions indicate that the image may contain a face, such as flared nostrils, raised eyebrows, wrinkled foreheads, and opened mouths. When a face is detected and a particular face model matches with a specific movement, the model is laid over the face, enabling face tracking to pick up further face movements. The state-of-the-art solutions usually combine several methods, extracting features, for example, to be used in machine learning or deep learning algorithms.

Face detection tools

There are dozens of face detection solutions, both proprietary and open-source, that offer various features, from simple face detection to emotion detection and face recognition.

Proprietary face detection software

Amazon Rekognition is based on deep learning and is fully integrated into the Amazon Web Service ecosystem. It is a robust solution both for face detection and recognition, and it is applicable to detect eight basic emotions like “happy”, “sad”, “angry”, etc.  Meanwhile, you can determine up to 100 faces in a single image with this tool. There is an option for video, and the pricing is different for different kinds of usage. 

Face++ is a face analysis cloud service that also has an offline SDK for iOS & Android. You can perform an unlimited amount of requests, but just three per second. It also supports Python, PHP, Java, Javascript, C++, Ruby, iOS, Matlab, providing services like gender and emotion recognition, age estimation, and landmark detection. 

They primarily operate in China, are exceptionally well funded, and are known for their inclusion in Lenovo products. However, bear in mind that its parent company, Megvii has been sanctioned by the US government in late 2019.

Face Recognition and Face Detection API (Lambda Labs) provides face recognition, facial detection, eye position, nose position, mouth position, and gender classification. It offers 1000 free requests per month.

Kairos offers a variety of image recognition solutions. Their API endpoints include identifying gender, age, facial recognition, and emotional depth in photo and video. They offer 14 days free trial with a maximum limit of 10000 requests, providing SDKs for PHP, JS, .Net, and Python.

Microsoft Azure Cognitive Services Face API allows you to make 30000 requests per month, 20 requests per minute on a free basis. For paid requests, the price depends on the number of recognitions per month, starting from $1 per 1000 recognitions. Features include age estimation, gender and emotion recognition, landmark detection. SDKs support Go, Python, Java, .Net, andNode.js.

Paravision is a face recognition company for enterprises providing self-hosted solutions. Face and activity recognition and COVID-19 solutions (face recognition with masks, integration with thermal detection, etc.) are among their services. The company has SDKs for C++ and Python.

Trueface is also serving enterprises, providing features like gender recognition, age estimation, and landmark detection as a self-hosted solution. 

Open-source face detection solutions

Ageitgey/face_recognition is a GitHub repository with 40k stars, one of the most extensive face recognition libraries. The contributors also claim it to be the “simplest facial recognition API for Python and the command line.” However, their drawbacks are the latest release as late as 2018 and 99.38% model recognition accuracy, which could be much better in 2021. It also does not have REST API.

Deepface is a framework for Python with 1,5k stars on GitHub, providing facial attribute analysis like age, gender, race, and emotion. It also provides REST API. 

FaceNet developed by Google uses Python library for implementation. The repository boasts of 11,8k starts. Meanwhile, the last significant updates were in 2018. The accuracy of recognition is 99,65%, and it does not have REST API. 

InsightFace is another Python library with 9,2k stars in GitHub, and the repository is actively updating. The recognition accuracy is 99,86%. They claim to provide a variety of algorithms for face detection, recognition, and alignment. 

InsightFace-REST  is an actively updating repository that “aims to provide convenient, easy deployable and scalable REST API for InsightFace face detection and recognition pipeline using FastAPI for serving and NVIDIA TensorRT for optimized inference.”

OpenCV isn’t an API, but it is a valuable tool with over 3,000 optimized computer vision algorithms. It offers many options for developers, including Eigenfacerecognizer, LBPHFacerecognizer, or lpbhfacerecognition face recognition modules.

OpenFace is a Python and Torch implementation of face recognition with deep neural networks. It rests on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering. 

Bottom line

Face detection is the first step for further face analysis, including recognition, emotion detection, or face generation. However, it is crucial to all other actions to collect all the necessary data for further processing. Robust face detection is a prerequisite for sophisticated recognition, tracking, and analytics tools and the cornerstone of computer vision.

Originally posted on SciForce blog.

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Data Quality and DataOps towards Customer Value

Data is an essential topic in today’s business world. Every business owner wants to talk about innovative ideas and the value that can flow from data. The data regarding markets, customers, agencies, other companies, and publishers are considered to be valuable resources. Statistics and data are only useful if they are of high quality.

The definition of data quality is so broad that it helps companies with different markets and missions to understand whether their data meets the standards. There are some major benefits of Data Quality that will help you to recognize the true values of high-quality data. Good data requires data governance, strict data management, accurate data collection, and careful design of control programs. For all quality issues, it is much easier and less costly to prevent data issues from happening. You can say that data quality is the key to being successful.

Gartner describes Data Ops as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.” Data Ops is about reorienting data management to be about value creation. The Data Ops mentality stresses cross-functional collaboration in data management, learning by doing, rapid deployment, and building on what works.

Gartner recommends three approaches to DataOps based on how an organization consumes data. They are,

Utility Value Proposition

By treating data as a utility that focuses on removing silos and manual effort when accessing and managing data. As such, data and analytics are readily available to all key roles. Because there are many relevant roles and not a single owner of the data, assign a data product manager to ensure data consumers’ needs are being met.

Enabler Value Proposition

For this value proposition, data and analytics support specific use cases such as fraud detection, analysis of supply chain optimization, or inter-enterprise data sharing. Product serving their use case.

According to Gartner, the enabler value proposition works best for teams supporting specific business use cases. “DataOps must focus on early and frequent collaboration with the business unit stakeholders who are the customers for a specific product serving their use case.”

• Collaboration is a key benefit of DataOps that we’ve explored extensively.
• Our DataOps Platform has functionality that will enable you to report on data team productivity and efficiency.

Driver Value Proposition

Use data and analytics to create new products and services, generate new revenue streams or enter new markets. For example, an idea for a new connected product emerges from your lab and must evolve into a production quality product for use by your customers. Use DataOps to link “Can we do this?” to “How do we provide an optimized, governed data-driven product to our consumers?”

Gartner explains that this is “the proposition that causes intractable challenges relating to data governance and the promotion of new discoveries into production.”

Conclusion

Many organizations are unaware of the importance of data in conducting business processes. It’s vital in providing management information about the business operations results. Because corporate data forms the basis of decision-making in an organization. It’s important that data is appropriate and effective to help make good decisions. Determining and enforcing appropriate data quality rules and regulations is the central key to the quality of data and testing. In the years to come, there will be an increase in data analysts, data analysis software, and companies that will structure the quality management of data. Delivering DataOps using each value proposition will foster collaboration between stakeholders and data implementers delivering the right value proposition with the right data at the right time.

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How to Use Data Science for Search Engine Optimization

Data science is one of the hottest topics in the market nowadays. It is one of those industries that has revolutionized the world. It associates two chief technologies, big data and artificial intelligence, and utilizes them to examine and process datasets. It also uses machine learning, which helps to strengthen artificial intelligence. Data science has thoroughly improved and modernized every industry it has touched, including marketing, finance, social media, SEO, etc. If you wish to excel in your profession, there is a high chance you will have to use data science with python. Data science assists SEO experts in countless ways, like personalizing the customer experience, understanding client requirements, and many other things. Following are some notable ways in which data science assists SEO specialists: 

Prediction

The prediction algorithms help in forecasting popular keywords. The fundamental approach for these algorithms is that it allows the SEO experts to make a primary assumption that if they stand first for certain specific keywords, then what would be their corresponding revenue inside a feasible error perimeter. It also helps in finding certain keywords and phrases that are related to the search. It predicts keywords that gather a larger audience and fit their requirements.

Generation

The task in SEO that takes up most of the time is content generation. High-grade content is valuable and expensive. There are numerous generation algorithms in python data science that could help create content automatically considering user demands. In most cases, it generates a draft that undergoes multiple updates as per requirements. Data science algorithms study previously given data and predict suitable content and trends according to it. Experience-based researches are fruitful since the content generated by these researches attracts larger masses of people.

Automation

SEO is hectic and demands a considerably large amount of time and manual effort. It includes many repetitive tasks like labeling images and videos. Numerous algorithms in data science lessen this kinds of manual work. An excellent case of such a set of algorithms would be TensorFlow, which helps in labeling images. This set also helps in optimizing all the attributes that eventually ease the efficiency of the whole project. This application of data science allows in generating sensible content by labeling advertisements, broken URLs, and unknown images. 

Selection

Data science with python supports SEO specialists recognize the quality of the data they have, which undeviatingly influences the insights they obtain. For obtaining meaningful insights, data scientists need suitable instruments. Data science provides a way to select the optimal source for seeking data and the best procedures to extract meaningful information from that source. It associates various algorithms that work simultaneously for the improvement of content quality.

Integration

Nowadays, SEO integrates with several digital marketing fields, like content marketing, CX management, CRO, sales, etc. When this happens, it is crucial for growth that the organization does not depend on any particular resolution for SEO. There is never a one-stop solution for it. Many factors are taken into consideration for SEO ranking. It is a cumulative mix of various portions that come into an association for concluding the expected traffic on the webpage.

Visualization

There are generally two approaches for analyzing data, hierarchical and visual. When someone employs the hierarchical approach to data, they usually miss out on several crucial points hidden in the insights. Using a data visualization approach can help in the following ways:

• Contrast and compare
• Process massive volumes of data at the system
• Speeds knowledge exploration
• Unveil secret queries
• Find well-known patterns and trends 

Conclusion

Data science with python is an enormously valuable field of science, that if used appropriately, can do wonders to the associated industries. Data science has improved every industry in uncountable ways. It has improved user experience exponentially. Experience gained through acquired data provides worthy insights. These insights are useful in many ways, like generating personalized content, labeling unknown data, selecting reliable sources, and visualizing analyzed results. Data science has been working in integration with several industries, including SEO. 

SEO has been improved via data science in many ways; it has answered many valuable questions to SEO specialists like, why is the ranking of some pages better than the others, what are the redundant mistakes in the content generation that are not visible to human users, what are the keywords or phrases that are users search on specific intervals? Data science eases work for SEO specialists via automation. This field of science has a long way in the market, and if used up to its complete potential it can surpass all human expectations.

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Ways Artificial Intelligence Impacts the Banking Sector

“75 percent of respondents at banks with over $100 billion in assets say they’re currently implementing AI strategies, compared with 46 percent at banks with less than $100 billion in assets,” UBS Evidence Lab reports.

Artificial Intelligence (AI) has become an integral part of the most demanding and fast-paced industries. The impact of AI in the investment banking industry and financial sector has been phenomenal and it is completely redefining the way they function, create products and services, and how they transform the customer experience. 

In this article, let’s explore a few key ways AI is impacting the investment banking sector.

Improving customer support

Customer satisfaction directly impacts the performance of any enterprise, including those in the investment banking industry. It directly shapes people’s perceptions of the financial institution’s brand. It also influences banks’ client targeting and retention efforts. One of the major issues users face is that financial institutions never seem to be open when they need them most.

For example, what if a customer’s account gets blocked during the holidays? Or, what if a customer wants to learn more about the bank loans later in the day when the employees have already clocked off? The customers’ money never sleeps. Therefore, financial institutions must focus on offering their clients the right services when they need them most.

The AI chatbots and voice assistants are the best for offering customer support. These sophisticated tools are available 24/7 irrespective of their time zone or location, customers can use chatbots for any task that doesn’t require human interaction, such as familiarizing themselves with the services, solving problems, and seeking answers to any question they may have. Most importantly, AI chatbots are constantly learning more about the customers by observing their previous interactions and browsing history in order to serve them with highly personalized user experiences.

Minimizing operating costs

Even though the investment banking industry and financial institutions already use the latest technologies to make their jobs safer and simpler, their employees still need to manage loads of paperwork daily. These kinds of time-intensive and repetitive tasks can cause an increase in operational costs and harm overall employee productivity, which might result in human error. AI eliminates these error-prone human processes.

For example, machine learning, automation tools, AI assistants, and handwriting recognition can streamline several aspects of human jobs. These tools can collect, classify, and enter customer data directly from their contracts and forms. This is a great opportunity for banks to leave manual and repetitive tasks to AI-backed machines and spend more time on creative, high-value works like serving customers with better, highly personalized services or finding new methods of enhancing client satisfaction.

Supporting customers to choose their credit and loans

Financial institutions still depend on factors like one’s credit score, credit history, revenue, and banking transactions to determine whether they’re creditworthy. This is exactly where AI can help, as its analysis goes far beyond the customer data. AI loan decision systems are taking help from ML to observe the patterns and behaviors that help a bank to determine whether a user can really be a good credit customer or not. AI makes credit decision systems more accurate and reliable.

Better regulatory compliance

AI also enhances the way banks impose their regulatory controls. The investment banking industry is one of the biggest regulated industries globally. All banks are required to have reasonable risk profiles to prevent major problems, offer good customer support, and identify the patterns in customer behavior. They rely on tools to identify and prevent the risk of financial crimes like money laundering.

With the growth of AI tools, investment banking has experienced a revolution in its efforts to offer safer and more reliable customer experiences. These pieces of software usually depend on cognitive fraud analytics that observes customer behaviors, track transactions, identify suspicious activities, and assess the data of various compliance systems. Even though these tools haven’t reached their full potential yet, they are already helping banks enhance their regulatory compliance and minimize unnecessary risk.

Enhances risk management

In banking, AI is a major game-changer when it comes to risk management. Financial institutes like the IB sector are prone to risk due to the type of data they handle every day. For instance, banks employ AI-powered solutions, which have the ability to analyze data in huge volumes and can quickly spot patterns from many channels. This helps predict and prevent credit risks and can identify individuals and businesses who might default on their obligation to repay their loans. It can also identify malicious acts such as identity theft and money laundering. AI tools and algorithms have revolutionized risk management in offering a safer and more trusted banking experience. Thus, it is clear that the impact of AI in the IB sector has enhanced risk management.

Wrapping Up      

As the world is pacing briskly towards complete digital transformation, advanced technologies like AI will have a greater impact on the banking sector in the future. The AI will offer more flexible and agile business models for the growing requirements in this digital world.

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Enhance Cognitive Bandwidth with Outsourced Web Research Services

The web research process is essential for businesses irrespective of the industry verticals they deal in. From global Corporates to small and medium-sized enterprises to aggregator startups, organizations need online research to take the data-driven approach. This helps them in making informed decisions and scale new heights.

Efficiently performed internet research enables stakeholders to perform competitor analysis. They can learn about the evolving market demographics, shifting consumer preferences, and other factors impacting business growth. Above all, the insights derived after analyzing data help the leaders to understand the demand and supply chain in the market.

This enables the businesses to ace their peers and gains a competitive advantage in the industry. They can identify the gaps and uncover unique opportunities for themselves. Last, but not least, companies can devise effective strategies as well as chalk out roadmaps that bring in growth. This consequently helps them to gain big wins in profit and get incremental ROI.

However, the web research function is a significant undertaking. It is a time-consuming and resource-intensive process that requires dedicated efforts. Otherwise, the analysis can be delayed leading to a slowed-down decision-making process. Managing it along with other core competencies becomes challenging for a majority of organizations.

On the other hand, hiring an in-house team is not always a feasible option. It not only involves a tiring recruitment process, but it also adds to operational expenditures substantially—in terms of technology implementation, employee salaries, infrastructural investments, etc. Instead, a smart option is to engage professional web research services.

Benefits of Engaging Professional Web Research Services

Apart from being a cost-efficient alternative, associating with outsourcing companies enables businesses to utilize their resources strategically. They can enhance the cognitive bandwidth of their employees as well as increase productivity. In addition to this, they can reap a host of other advantages as mentioned here.

• Professional Excellence

The offshoring vendors have a pool of competent professionals hired from around the world. They have hands-on experience in the web research process and know what it takes to cater to the client’s needs. These professionals work as an extended in-house team to help businesses achieve the set goals and strive to deliver excellence in every endeavor.

• Technological Competence

Equipped with the latest technologies, streamlined processes, and a time-tested blend of manual workflows, the external vendors can efficiently scrape out large volumes of data from any number of resources. They leverage the right-fit tools to exceedingly meet the client’s requirements. If needed, the professional providers can also alter their operational approach.

• Industry Compliant Practices

There are numerous data-related laws that have to be taken care of while dealing with such tasks. Business as usual, they stay updated with all the latest norms, rules, and legislation. So, they abide by all such regulations including GDPR, HIPAA, ADA, DDA, CGPA, and so on. All their practices are industry compliant and follow stringent data security protocols to ensure its confidentiality.

• Quality with Accuracy

These two are the most important factors that are considered by businesses before outsourcing such ancillary tasks. The fact is acknowledged by the professional providers and lays focus on quality as well as the accuracy of the outcomes. Having in-built quality check systems, their QA team ensures that the results are error-free. Also, they assure up to 99.99% accuracy.

• Scalability and Flexibility

A conspicuous advantage of engaging professional services is the versatility they provide. This means that the offshoring companies offer the ease of scaling their operations upwards or downwards based on the client’s needs. They have flexible delivery models to ensure that the outcomes are efficient and thorough across different industry verticals.

• Customized Solutions

Every business has distinct requirements. The fact is well addressed by the professional providers. After properly assessing and understanding the client’s pain points, the outsourcing companies offer them a customized and comprehensive suite of offerings including online research services, market research services, internet research services, etc.

Wrapping Up

If you are looking to prospect web research services, you can find a good bunch of options. Make sure that the outsourcing company understands business requirements and project’s needs as well as aligns their outcomes with your unique goals. Therefore, your major goal should be finding the right partner!

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DSC Weekly Digest 21 June 2021

One of the more underappreciated problems of working with big data systems, machine learning systems, or knowledge graphs is the fact that the number of classes (types of things) can very quickly number in the hundreds or even thousands. If the data involved in these systems comes primarily from external data stores, this can be problematic even with service interfaces, but where the issue becomes quickly unmanageable is in the realm of user interfaces and user experience.

To give an example, a typical ERP such as Salesforce may contain data for people, locations, transactions, accounts, products, and so on, often numbering in the dozens of different kinds of entities being tracked. All too often, the attitude is that this information comes from databases, but the reality is that somewhere along the lines, someone – a data entry person, an account manager, a customer, a shipper, somebody – will need to enter that information into the computer in the first place. This is actually a source of one of the biggest problems in enterprise data management today.

Why? All too often, the data that goes into one data store represents a very narrow (and almost always programmer-designed) view of various kinds of data. In practice, people do not have access to the underlying data but rather are now reliant upon data services – web services, mainly – that transform an existing record into a frequently lossy JSON, XML or JSON file, losing metadata along the way. This means that keys often become poorly transcribed or lose their context, it means that the ability to add additional properties becomes a major, expensive headache. What’s worse, this process when repeated across dozens of systems, creates an impenetrable thorny wall that reduces or even eliminates any kind of flexibility.

One of the benefits of semantic-based systems (knowledge graphs) is that you can solve several data engineering problems at once. Such knowledge graphs are highly connected, but those connections can be traversed with query languages (SPARQL, GQL, GraphQL, Tinkerpop, and so forth) and can be extended with very little effort. Moreover, it becomes possible to infer structure from data, to hold data connected in multiple ways, and to readily handle true temporality, not just the transactional log focus that’s typical with SQL databases.

Additionally, because of this, systems can use inferential reasoning to be able to “ask” the user to provide just the information that is needed, through generated UI elements. A person in your system changes addresses? Intelligent interface design can bring up just the information needed for updating (or adding) that address, and can even tell by UI cues whether to create new address records or update existing ones – without the need for a programmer to create such a UI screen for this. Similarly, machine learning applications can take a hand-drawn sketch (perhaps with dragged elements) and turn it into a user interface trivially.

This approach has many key benefits – consolidation and significant reduction in duplicate (or near-duplicate) data, major reductions in the time and cost of building applications, reducing or even eliminating the need for complex forms (and perhaps the need for everyone to constantly re-enter resume information), among many others. Additionally, such efforts can create large-scale datastores that dynamically respond to changes in models without overt and expensive software production efforts.

It is the ability to better control the entry of data into the data ecosystem in the first place, not just clever chatbots or expensive data-mining efforts, that will enable data-driven companies to succeed. Control the input, the ingestion, of data so that it is consistent with the underlying models early in the process, and you are well on your way to reducing everything from data cleanliness, master data management, feature engineering and even data analysis systems. However, to do that, it’s time to move beyond SQL and start embracing the graph.

In media res,
Kurt Cagle
Community Editor,
Data Science Central

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Stroke Prediction using Data Analytics and Machine Learning

• Data-based decision making is increasing in medicine because of its efficiency and accuracy.
• One branch of research uses Data Analytics and Machine Learning to predict stroke outcomes.
• Models can predict risk with high accuracy while maintaining a reasonable false positive rate.

Stroke is the second leading cause of death worldwide. According to the World Health Organization [1], 5 million people worldwide suffer a stroke every year. Of these, one third die and another third are left permanently disabled. In the United States, someone has a stroke every 40 seconds and every four minutes, someone dies [2]. The aftermath is devastating, with victims experiencing a wide range of disabling symptoms including sudden paralysis, speech loss or blindness due to blood flow interruption in the brain [3]. The economic burden to the healthcare system in the United States amounts to about $34 billion per year in the US [4]. An additional $40 per year is spend on care for elderly stroke survivors [5]. 

Despite these alarming statistics, there is a glimmer of hope: strokes are highly preventable if high risk patients can be identified and encouraged to make lifestyle modifications. While some risk factors like family history, age, gender, and race cannot be modified, it is estimated that 60 to 80% of strokes could be prevented through healthy lifestyle changes like losing weight, smoking cessation, and controlling high cholesterol and blood pressure levels [6, 7].  However, despite the wealth of data on these risk factors, traditional, non-DS methods typically perform quite poorly at predicting who will have a stroke and who will not; Identifying high risk patients is a challenge  because of the complex relationship between contributory risk factors.

Predicting Stroke Outcome with Data Analytics and Machine Learning

Various DA and ML models have been successfully applied in recent years to assess stroke risk factors and outcomes. They include evaluating a mixed-effect linear model to predict the risk of cognitive decline poststroke [8] and developing a deep neural network (DNN) model, applying logistic regression and random forest to predict poststroke motor outcomes [9].  In another study, researchers created a model capable of predicting stroke outcome with high accuracy and validity; The research applied an unbalanced dataset containing information for several thousand individuals with known stroke outcomes. Various algorithms including decision trees, Naïve Bayes and Random Forest were assessed, with random Forest classifier the most promising, predicting stroke outcome with 92% accuracy. The following table shows results for various methods employed in the study [10]:

Improving the False Negative Rate with ML

One of the major challenges with attempting to predict any major disease like stroke is the high cost of false negatives. A false negative result is where the patient has the disease, but the test (or predictive tool) does not identify the patient as having the disease (or risk for the disease). Unlike false negatives in a business setting, false negatives in medicine can have deadly consequences. If someone gets a false negative and is told they are not at risk for a major disease, then they are not in a position to make informed lifestyle choice–putting their life in danger. Historically, false negative rates from traditional approaches exceeds 50%, but this has been reduced to less than 20% by applying machine learning tools [11].

The Future of Stroke Prediction

Data Science is the optimal solution to dealing with the approximately 1.2 billion clinical documents being produced in the United States every year [12]. The results from DS based stroke prediction research look promising. The tools are more reliable and valid than traditional methods; They can also be acquired conveniently and at a low cost [11]. As data science continues to grow within medicine, it  will open new opportunities for more informed healthcare and prevention of deaths from major diseases like stroke.

References

Image: mikemacmarketing / original posted on flickrLiam Huang / clipped and posted on flickr, CC by 2.0 via Wikimedia Commons

[1]  Stroke, Cerebrovascular accident | Health topics – WHO EMRO

[2] Heart Disease and Stroke Statistics—2020 Update: A Report From the …

[3] The science of stroke: Mechanisms in search of treatments

[4] American Heart Association Statistics Committee and Stroke Statisti…

[5] Care received by elderly US stroke survivors may be underestimated.

[6] Preventing Stroke: Healthy Living Habits | cdc.gov.

[7] The science of stroke: Mechanisms in search of treatments,

[8] Risk prediction of cognitive decline after stroke.

[9] Prediction of Motor Function in Stroke Patients Using Machine Learn…,

[10] Stroke prediction through Data Science and Machine Learning Algorithms 

[11] A hybrid machine learning approach to cerebral stroke prediction ba… dataset

[12] Health story project

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Top Eleven Skills to Look Out For While Hiring DevOPS Engineer

Companies that incorporate DevOps practices get more done. It is as simple as that. The technical benefits include continuous delivery, easier management, easier to manage, and faster problem-solving. In addition to this, there are cultural benefits like more productive teams, better employee engagement, and better development opportunities.

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Why Data Monetization is a Waste for Most Companies

Here, let’s start this blog with some controversy:

“For most organizations, data monetization is a total waste of time”

I’ve been having lots of conversations recently about where the Data and Analytics organization should report.  Good anecdotal insights, but I wanted to complement those conversations with some raw data.  So, I ran a little LinkedIn poll (thanks to the nearly 2,000 people who responded to the poll) that asked the question: “Based upon your experience across different organizations, where does the Data & Analytics organization typically report TODAY?”  The results are displayed in Figure 1.

Figure 1: “Where does the Data & Analytics organization typically report TODAY?”

The poll results in Figure 1 were incredibly disappointing but at least they help me understand why a data monetization conversation for most organizations is a total waste of my time.

From the poll, we learn that in 54% of organizations, the Data & Analytics organization reports to the Chief Information Officer (CIO). The data monetization conversation is doomed when the Data & Analytics organization reports to the CIO. Why? Because the Data & Analytics initiatives are then seen as technology efforts, not business efforts, by the business executives.  And if data and analytics are viewed as technology capabilities and not directly focused on deriving and driving new sources of customer, product, and operational value, then there is no data monetization conversation to be had.  Period.

Arguments for why the Data and Analytics function SHOULD NOT report to the Chief Information Officer (CIO) include:

• The CIO’s primary focus is on keeping the operational systems (ERP, HR, SFA, CRM, BFA, MRM) up and running. If one of these systems go down, then the business grinds to a halt – no orders get taken, no products get sold, no supplies get ordered, no components get manufactured, etc.  Ensuring that these systems never go down (and stay safe from hackers, cyberattacks, and ransomware) is job #1 for the CIO.  Unfortunately, that means that data and analytics are second class citizens in the eyes of the CIO as data and analytics are of less importance to the critical operations of the business.
• And while everyone is quick to point out that the CIO typically has responsibility for the data warehouse and Business Intelligence, the Data Warehouse and BI systems primarily exist to support the management, operational, and compliance reporting needs of the operational systems (see SAP buying Business Objects).
• Finally, the head of the Data & Analytics organization (let’s call them the Chief Data & Analytics Officer or CDAO) needs to be the equal to the CIO when it comes to the senior executive discussions and decisions about prioritizing the organizations technology, data, and analytics investments. If the CDAO reports to the CIO, then the data and analytics investments could easily take a back seat to the operational system investments.

Let’s be very honest here, packaged operational systems are just sources of competitive parity.  I mean, it’s really hard to differentiate your business when everyone is running the same SAP ERP, Siebel CRM, and Salesforce SFA systems.  Plus, no one buys your products and services because you have a better finance or human resources system. 

So, organizations must elevate the role of the data and analytics organization if they are seeking to leverage their data to derive and drive new sources of customer, product, and operational value.  Consequently, the arguments for why the Data and Analytics function (or CDAO) SHOULD report to the CEO, General Manager, or Chief Operating Officer include:

• In the same way that oil was the fuel that drove the economic growth in the 20th century, data will be the driver of economic growth in the 21st century. Data is no longer just the exhaust or byproduct from the operations of the business.  In more and more industries, data IS the business. So, the CDAO role needs to be elevated as an equal in the Line of Business executives to reflect the mission critical nature of data and analytics.
• One of the biggest challenges for the Data and Analytics function is to drive collaboration across the business lines to identify, validate, value, and prioritize the business and operational use cases against which to apply their data and analytics resources. Data and analytics initiatives don’t fail due to a lack of use cases, they fail because they have too many.  As a result, organizations try to peanut butter their limited data and analytic resources across too many use cases leading to under-performance in each of them.  The Data and Analytics function needs to stand as an equal in the C-suite to strategically prioritize the development and application of the data and analytic assets.
• A key priority for the Data and Analytics function is to acquire new sources of internal (social media, mobile, web, sensor, text, photos, videos, etc.) and external (competitive, market, weather, economic, etc.) data that enhances the data coming from the operational systems. These are data sources that don’t typically interest the CIO. The Data and Analytics function will blend these data sources to uncover, codify, and continuously-enhance the customer, product, and operational insights (predicted propensities) across a multitude of business and operational use cases (see the Economics of Data and Analytics).
• It is imperative that all organizations develop a data-driven / analytics-empowered culture where everyone is empowered to envision where and how data and analytics can derive and drive new sources of value. That sort of empowerment must come from the very top of the organization.  Grassroots empowerment efforts are important (see Catalyst Networks), but ultimately it is up to the CEO and/or General Manager to create a culture where everyone is empowered to search for opportunities to exploit the unique economic characteristics of the organization’s data and analytics.

To fully exploit their data monetization efforts, leading-edge organizations are creating an AI Innovation Office that is responsible for:

• Testing, validation, and training on new ML frameworks,
• Professional development of the organization’s data engineering and data science personnel
• “Engineering” ML models into composable, reusable, continuously refining digital assets that can be re-used to accelerate time-to-value and de-risk use case implementation.

The AI Innovation Office typically supports a “Hub-and-Spoke” data science organizational structure (see Figure 2) where:

• The centralized “hub” data scientist team collaborates (think co-create) with the business unit “spoke” data scientist teams to co-create composable and reusable data and analytic assets. The “Hub” data science team is focused on the engineering, reusing, sharing, and the continuous refinement of the organization’s data and analytic assets including the data lake, analytic profiles, and reusable AI / ML models.
• The decentralized “spoke” data science team collaborates closely with its business unit to identify, define, develop, and deploy AI / ML models in support of optimizing the business unit’s most important use cases (think Hypothesis Development Canvas). They employ a collaborative engagement process with their respective business units to identify, validate, value, and prioritize the use cases against which they will focus their data science capabilities.

Figure 2:  Hub-and-Spoke Data Science Organization

The AI Innovation Office can support a data scientist rotation program where data scientists cycle between the hub and the spoke to provide new learning and professional development opportunities. This provides the ultimate in data science “organizational improv” in the ability to move data science team members between projects based upon the unique data science requirements of that particular use case (think Teams of Teams).

Finally, another critical task for the AI Innovation Office is to be a sponsor of the organization’s Data Monetization Council that has the corporate mandate to drive the sharing, reuse, and continuous refinement of the organization’s data and analytic assets. If data and analytics are truly economic assets that can derive and drive new sources of customer, product, and operational value, then the organization needs a governance organization with both “stick and carrot” authority for enforcing the continuous cultivation of these critical 21st century economic assets (see Figure 3).

Figure 3:  Role of Data Monetization Governance Council

A key objective of the Data Monetization Governance Council is to end data silos, shadow IT spend, and orphaned analytics that create a drag on the economic value of data and analytics. And for governance, to be successful, it needs teeth.  Governance must include rewards for compliance (e.g., resources, investments, budget, and executive attention) as well as penalties for non-compliance (e.g., withholding or even clawing back resources, investments, budget, and executive attention).  If your governance practice relies upon cajoling and begging others to comply, then your governance practice has already failed.

So, in summary, yes, for most organizations (54% in my poll), the data monetization conversation is a total waste of time because data monetization conversation doesn’t start with technology but starts with the business. That means that the Data and Analytics function must have a seat in the C-suite, otherwise the data monetization conversation truly is a waste of time.

By the way, I strongly recommend that you check out the individual comments from the nearly 2,000 folks who responded to my LinkedIn poll.  Lots of very insightful and provocative comments.  Yes, that is the right way to leverage social media!!

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